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International Workshop on Artificial Neural Networks

IWANN 1995: From Natural to Artificial Neural Computation pp 427–432Cite as

Improving back-propagation: Epsilon-back-propagation

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Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 930))

Abstract

A modified version of back-propagation learning algorithm is introduced. This new algorithm called epsilon-back-propagation allows a neural network to learn faster or al least as good as back-propagation. Experimental data is given in order to compare both methods.

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References

  1. Hecht-Nielsen, R., Neuncomputing, Addison-Wesley Publishing Co., Reading, MA, 1990.

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  2. Hertz, J., A. Krogh and R. G. Palmer, Introduction to the Theory of Neural Computation, Addison-Wesley Publishing Co., Redwood City, CA, 1991.

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  3. Kröse, B.J. and P. van der Smagt, An introduction to Neural Networks, Dept. of Computer Systems, University of Amsterdam, 1993.

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  4. Rich, E., and K. Knight, Artificial Intelligent Systems, McGraw-Hill, New York, 1991.

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  5. Sandoval, Carlos A., A Modified Learning Algorithm for Backpropagation Networks, Master Thesis, University of Texas at el Paso, 1993.

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Author information

Authors and Affiliations

  1. Computer Science Department, ITESM-CEM, Carretera a Lago de Guadalupe KM 3.5, Atizapán de Zaragoza, C.P. 52926, Edo. de Mexico, México

    Luis A. Trejo & Carlos Sandoval

Authors
  1. Luis A. Trejo
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  2. Carlos Sandoval
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Editor information

José Mira Francisco Sandoval

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© 1995 Springer-Verlag Berlin Heidelberg

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Trejo, L.A., Sandoval, C. (1995). Improving back-propagation: Epsilon-back-propagation. In: Mira, J., Sandoval, F. (eds) From Natural to Artificial Neural Computation. IWANN 1995. Lecture Notes in Computer Science, vol 930. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-59497-3_205

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  • DOI: https://doi.org/10.1007/3-540-59497-3_205

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-59497-0

  • Online ISBN: 978-3-540-49288-7

  • eBook Packages: Springer Book Archive

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